{"id":"W4324329974","doi":"10.21203/rs.3.rs-2635577/v1","title":"A Procedural Approach for Finding Kinetic Parameters of Polypropylene Gasification in Super Critical Water Using Genetic Algorithm","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Thermal and Kinetic Analysis","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Nuclear Laboratories; National Research Council Canada","funders":"National Research Council Canada","keywords":"Polypropylene; Genetic algorithm; Kinetic energy; Algorithm; Computer science; Materials science; Mathematical optimization; Mathematics; Physics; Composite material","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001915419,0.0002411799,0.0005128495,0.000702808,0.000132468,0.0001425741,0.0005598944,0.0002777076,0.0001112546],"category_scores_gemma":[0.0005860238,0.000183579,0.000192246,0.0004094349,0.0003730103,0.00007821318,0.0006314043,0.0004760412,0.00003015784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001931448,"about_ca_system_score_gemma":0.0001892486,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001241981,"about_ca_topic_score_gemma":0.00001024217,"domain_scores_codex":[0.9962071,0.0004474333,0.0006402912,0.0008465087,0.0008947959,0.0009638819],"domain_scores_gemma":[0.998458,0.0003310195,0.00006815899,0.0005302306,0.0004923389,0.0001202696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002511004,0.0005483424,0.001855158,0.008432833,0.0000679119,0.00002852659,0.002929827,0.04420757,0.9371538,0.0001905594,0.00006671458,0.004267657],"study_design_scores_gemma":[0.0006456878,0.0003076395,0.009698677,0.001106843,0.0001287311,0.00001286953,0.001840106,0.7033671,0.2782437,0.004021687,0.00001705904,0.0006099446],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9277961,0.000213346,0.06978668,0.0003704361,0.0001252824,0.001446906,0.0001932016,0.00004657112,0.00002142159],"genre_scores_gemma":[0.8559773,0.00003116789,0.1430168,0.000007838245,0.0001176378,0.0005610361,0.0001360069,0.00005310198,0.00009911235],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6591595,"threshold_uncertainty_score":0.7486135,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1801497541752132,"score_gpt":0.4150711610535195,"score_spread":0.2349214068783064,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}